Results for “wcag-2-2”

Predicts the highest-impact next action for your project by running a 5-agent meta-DAG pipeline. Gathers project signals automatically (git, recent files, port-daddy, CLAUDE.md), then runs sensemaker → decomposer → skill-selector + premortem → synthesizer. When execution is approved, convert each predicted node into a skillful node prompt using skillful-node-prompt + skillful-subagent-creator, prefer live WinDAGs visualization backed by POST /api/execute and /ws/execution/:id, and fall back to ASCII only when live visualization is unavailable. Activate on: "what should I do", "what's next", "next move", "/next-move", "where should I focus", "what's the highest impact thing right now". NOT for: creating skills, debugging one specific bug, or promising topology-specific runtime behavior the current server cannot execute.

Build WinDAGs — the orchestration platform where AI agents accumulate genuine expertise through DAGs of skillful agents. Covers DAG design, execution engines, meta-DAG architecture, skill selection, dynamic mutation, visualization, and deployment. Activate on "windags", "agent DAG", "DAG of agents", "workflow orchestration", "agent pipeline", "dynamic DAG", "meta-DAG", "build windags", "implement windags". NOT for understanding WHY decisions were made (use windags-avatar), creating individual skills (use skill-architect), or managing skill libraries (use windags-librarian).

Diagnoses WSL2 and Cursor/Claude performance issues on Windows, including memory, swap, disk, caches, and configuration, then recommends safe fixes.

Derives a comprehensive test plan from user stories and acceptance criteria, then writes unit, integration, and end-to-end test cases. Flags issues in prior artifacts and declares UAT readiness.

Takes raw whole-genome sequencing FASTQ files or a pre-existing VCF through variant calling, quality control, and polygenic risk score computation using the PGS Catalog.